Method of analyzing points of interest with probe data

ABSTRACT

A method of analyzing points of interest ( 22 ) using traces from probe data is provided. The method includes providing a database of a digital vector map ( 18 ) configured to store a plurality of traces ( 1′ - 14′ ) representing roads. The method further includes collecting probe data from vehicles traveling along the traces. Then, bundling a group of select traces ( 2′, 5′, 7′, 9′, 11′ ) having routes with a common origin ( 20 ) and at least one divergence point ( 24, 1 ) downstream from the origin ( 20 ) and building a database of vehicle maneuvers over the routes. Further, computing average speeds and delay times of a random population of vehicles traversing the vehicle maneuvers. Further yet, computing average speeds and delay times of all vehicles traversing the routes. Then, comparing the computed results from the random population of vehicles with the computed results from all vehicles traversing said routes.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to methods for analyzing points ofinterest, and more particularly to methods of analyzing points ofinterest with Global Positioning System (GPS)-enabled devices.

2. Related Art

Points of interest (POIs) are often analyzed via tabular information,such as via manual research; via directories of restaurants in a chainwith their addresses; points supplied by customers, third parties,address lists, and the like, wherein the points of interest are assigneda coordinate (latitude/longitude) via manual research or automated meanssuch as geocoding (the use of a known address and map match to assign anapproximate coordinate). Unfortunately, the results can be fraught witherrors, such as due to human error; furthermore, not all addresses areavailable on point lists, may have been changed, or the map itself mayhave incorrect street names or numbers, leading to incorrect locations.Further, rating of the POIs is typically manual, and thus, generallyproves difficult and costly. In addition, the manual data gathered canbecome dated in a relatively short period of time, thereby rendering thedata obsolete and increasingly inaccurate.

SUMMARY OF THE INVENTION

In accordance with one aspect of the invention, a method of analyzingpoints of interest using traces from probe data is provided. The methodincludes providing a database of a digital vector map configured tostore a plurality of traces representing roads and collecting probe datafrom vehicles traveling along the traces. Then, bundling a group ofselect traces having routes with a common origin and at least onedivergence point downstream from the origin and building a database ofvehicle maneuvers over the routes. Further, computing average speeds anddelay times of a random population of vehicles traversing the vehiclemaneuvers. Further yet, computing average speeds and delay times of allvehicles traversing the routes. Then, comparing the computed resultsfrom the random population of vehicles with the computed results fromall vehicles traversing said routes.

Upon comparing the computed results from the random population ofvehicles with the computed results of all vehicles traversing theselected routes, statistically probable differences may be discerned.Accordingly, POIs are able to be identified by noting the differences invehicle behavior over the selected routes.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects, features and advantages of the invention willbecome more readily appreciated when considered in connection with thefollowing detailed description of presently preferred embodiments andbest mode, appended claims and accompanying drawings, in which:

FIG. 1 illustrates a selected bundle of traces to be analyzed from anavigable street network database;

FIGS. 2A-2D illustrate examples of average speeds and delay times ofvehicles over a select portion of the bundles of traces of FIG. 1;

FIG. 3 illustrates another selected bundle of traces to be analyzed;

FIGS. 4A-4D illustrate the average speed and delay time of vehicles overtwo distinct points of the selected bundle of FIG. 3; and

FIGS. 5A-5B illustrate further statistical analysis of data taken at oneof the distinct points of FIGS. 4A-4D.

DETAILED DESCRIPTION OF PRESENTLY PREFERRED EMBODIMENTS

In accordance with one aspect of the invention, information is obtainedfrom global behavior of vehicles traveling along a navigable streetnetwork, wherein the street network is defined by a plurality of traces.The information is useful to assess specific behavior of the vehicles,and thus, can be used to determine where particular points of interest(POIs) exist along the navigable street network. The POI can bepre-existing, or new. The information gathered can be obtainedsubstantially real-time, and thus, the information is current andreliable. Further, since the navigable street network undergoes dynamicchange, the changes that occur can be monitored and processed in aneconomical manner, without need for manual data gathering. Theinformation can be used to determine the decision patterns of travelers,whether they be utilizing motorized vehicles, bicycles, pedestriantravel, or otherwise. Accordingly, the invention is not limited toassessing the behavior of motorized vehicles.

Referring in more detail to the drawings, FIG. 1 illustrates an exampleof how a database of traces can be bundled and selected for analysisfrom a portion of a navigable street network 10, by way of example andwithout limitation. The bundled traces exemplified have been assignedend points enumerated 1-4. Each of the traces originates from a commonlocation or origin 12 and pass through a common intersection 14. As canbe seen some of the traces share travel paths over a portion of theirdistance, such as exhibited by trace bundles 3 and 4 until they reach adivergence point 16. Accordingly, when gathering data for vehiclestraveling along trace bundles 3 and 4, it is important that thegeographic extent of the data collected from vehicles traveling alongthese bundles be obtained far enough along their respective paths todetect the decisions made by the vehicles upon reaching the divergencepoint 16 in the bundles 3 and 4. In addition, upon reaching thedivergence point 16, as can be imagined, vehicles wishing to travelalong trace bundle 3 may be delayed at the divergence point 16 for anynumber of reasons. For example, some vehicles may have to wait for aline of vehicles prior to turning, or they may have to wait for anindicator signal.

In order to assess the travel behavior of the vehicles traveling alongthe bundled traces 1-4, the maneuvers of the vehicles traveling alongthe bundled traces 1-4 can be analyzed. As shown in FIGS. 2A-2D, whereinFIG. 2A corresponds to bundled trace 1, FIG. 2B corresponds to bundledtrace 2, FIG. 2C corresponds to bundled trace 3, and FIG. 2D correspondsto bundled trace 4, an average speed profile of the vehicles (column L)and an average delay time profile of the vehicles (column R) over thebundled traces 1-4 can be obtained via probe data received from thevehicles. Any suitable statistical program application can be used togenerate the averaged information. As can be seen, the informationobtained for vehicles traveling along trace bundle 1 indicates that thevehicles turning left come to a stop or near stop and then resume aspeed immediately thereafter approximating the speed prior to making theturn. In contrast, the vehicles traveling along trace bundle 2 behavedifferently than those traveling along trace bundle 1, wherein thevehicles traveling along trace bundle 2 slow slightly from their top endspeed while traveling through the intersection 14 and then resumeimmediately thereafter their top end speed. In further contrast, thevehicles traveling along trace bundle 3 exhibit the slowest averagespeed at the intersection 14 to make a right hand turn, then speed upslightly, followed by another decline in average speed to make a lefthand turn at the divergence point 16. Then, for the vehicles travelingalong trace bundle 4, the behavior at the intersection 14 is markedlydifferent from those desiring to travel along trace bundle 3, whereinalthough the vehicles traveling along trace bundle 4 slow their averagespeed to make a right hand turn at the intersection, the decline inaverage speed is not nearly as great as those traveling along tracebundle 3. Accordingly, it is important to be able to distinguish betweenthe vehicles traveling along the traces in bundles 3 and 4 in order toobtain meaningful conclusions regarding their behavior. With thedatabase of maneuvers over the traces 1-4 now constructed, probe dataobtained from vehicles traveling the different trace bundles 1-4 can beused to assess POIs attracting the vehicles and likewise, POIs can bepopulated or verified on the trace bundles 1-4 to bring them to theattention to the users of Global Positioning System (GPS)-enabledpersonal navigation devices, such as those manufactured by TomTom NV(www.tomtom.com). However, any suitable device with GPS functionalitymay be used, including handheld devices, Personal Digital Assistants(PDAs), mobile phones, and the like.

In an example of how a database of maneuvers over a selected group oftraces on a navigable street network 18 can be utilized, we now refer toFIG. 3, which depicts a database of bundled traces 1′-14′ exiting acommon location, wherein the location is represented as an airport 20,by way of example and without limitation. It should be recognized that,for discussion purposes, the illustration is simplified, and that thebundled traces can be as complex as necessary to encompass the areadesired for study. Each trace path 1′-14′ is associated with thesequence of maneuvers that it follows upon exiting the airport 20. Thesequence of maneuvers can be continued until the trace ends; until thetrace returns to the airport 20, such as often occurs with taxis orbuses; until the trace extends beyond a predetermined geographicallimit, or until the trace exhibits a marked decrease in travel byvehicles, for example. Of course, other constraints can be used todetermine the traces and how far to extend analysis thereof.

In our example, we note that starting with the exit of the airport 20,that traces 1′ and 2′ are the only possible decisions for vehicles totravel. Upon study, we learn from probe data received that the vastmajority of vehicles leaving the airport 20 continue along trace 2′, andthat only slight minority travel along trace 1′. So, for purposes ofassessing POIs for vehicles leaving the airport 20, we discount thosevehicles electing to travel trace 1′, and continue monitoring probe datafrom those vehicles traveling along trace 2′. We continue this line ofreasoning until there is no one favored trace of travel over another,and by doing so, we learn from probe data that the most favored tracestraveled by vehicles are 2′, 5′, 7′, 9′ and 11′, and that upon reachingthe intersection (I) at 12′, 13′ and 14′, there is no clear favoredtrace traveled by vehicles exiting the airport 20. And so, for ourspecific purpose of vehicle behavior study, we elect to study theselected series of maneuvers (referred to as “route”) of the vehiclestraveling the probe traces 2′, 5′, 7′, 9′ , 11′ (referred to as “group”)through the maneuver ending at trace 11′.

In order to determine POIs located along the group 2′, 5′, 7′, 9′ , 11′of study, and in our example, a POI being represented as a hotel 22, analgorithm is used to compare the behavior in maneuvers (speed throughthe maneuver, stop time at decision point) between an overall randompopulation of vehicles and vehicles leaving airport 20, referred to asthe airport group. If the behavior between the two populations ofvehicles diverge such that it is statistically probable that they aredifferent, then a POI can be determined.

As illustrated in FIGS. 4A-4D, by way of example, it can be readilydetermined that there is a POI (hotel 22) at the maneuver for trace 9′,while there is no POI at the maneuver for trace 5′. The FIGS. 4A-4B showthe average speed (column L) and the average time delay (column R) ofthe bundled trace 5′ for the overall random population of vehicles (FIG.4A) and the airport group (FIG. 4B). In these figures we add the meandelay time in seconds (μ), and the median or 50^(th) percentile delaytime, also in seconds (M), for illustrative purposes, In comparing thetwo vehicle populations, it is apparent that there is little differencein their behavior. Though there is some difference, it is slight, andcould be attributed to such things as statistical sampling, differencesin vehicle types and behavioral differences due to things that havenothing to do with a POI, such as taxi cabs driving at different speedsor public transportation restricted to certain lanes, for example.

In contrast, the bundled trace 9′, as shown in FIGS. 4C-4D wherein theaverage speed (column L) and the average time delay (column R) for theoverall random population of vehicles (FIG. 4C) and the airport group(FIG. 4D) are shown, it is apparent that there is a substantialdifference between the behavior of the separate populations which can beinferred by statistical methods. As can be clearly seen from thedramatic drop in the speed profile of the selected airport group, thereis a detected POI approximately 65 meters prior to a divergence point 24where trace 9′ diverges from trace 8′. This discernible drop in speed,however, does not appear for the overall random population shown in FIG.4C. Accordingly, these illustrations show the value of comparingvehicles within the specific target group versus the larger randompopulation.

Of course, depending on the nature of the POI, the number of vehicles inthe group stopping at the POI could vary. As such, in accordance withthe invention, additional statistical analysis can be performed on theparticipants to increase the sensitivity in detecting POI. For example,in another embodiment, skew analysis (third moment) and kurtosis (fourthmoment) of the delay profile can be used to determine that a POI isoccurring for some vehicles along the route. In looking at the skewanalysis, we look for an increased forward component than that of theoverall random population of vehicles within the bundle. The forwardmoment indicates that a small subset of participants in the route arestopping longer than is typical. Similarly, there is a likely POI if thekurtosis is flatter (platykuric, having a wide and generally flat peakaround the mean), thereby not having sudden peaks, for the vehiclesleaving airport than for the overall random population of vehicles (thecontrol group). The likelihood of a POI, thus, can be calculated bymultiplying the likelihood values derived from a single statisticalmodel using mean and standard deviation, as well as the additionalvalues obtained from analyzing the skew and kurtosis.

In accordance with another aspect of the invention, to further pin pointPOIs, the statistical analysis can be performed at different times todetect patterns of behavior that occur during different times. Forexample, the probe data can be obtained during different times of theday, different times of the week, different times of the month, orduring different times of the year. In the case of the airport example,the vehicle traffic is typically greater during times later in the day,and thus, may not correspond with rush hour traffic which exhibits adifferent profile. In these cases, the time of day characteristics ofthe selected control group should be extended to the general populationto be compared. This can be done by comparing the group behavior againstthat of the randomized subset of the general population, selected tohave the same time of day statistical profile.

In accordance with another aspect of the invention, different analysistechniques can be used to interpret the data. For example, a profile ofspecific stops at a location that exceed a duration threshold of apredetermined period of time, such as 2 minutes, can be generated. Asshown in FIG. 5, the airport bundle 9 in 5B exhibits delays in timedistinct from the intersection 24, thereby further evidencing andcorroborating information that there is POI, and in addition,identifying the location(s) of the POI within the route. For example, aswith the evidence in FIG. 4D, column L indicating a slowdown at 65meters prior to the intersection 24, the delay profile detected viaprobe data indicates a spike 65 meters prior to the intersection 24.Accordingly, the location of the original maneuver and intersection cannow be used to position an entrance (E) to the POI within the streetnetwork 18.

The entrance E to the POI can be added automatically to the database, orit can be added manually upon verification, such as via aerialphotography, satellite imagery, business and social networking websites,or city plans and maps, for example. Manual editing may be used innaming and deriving type or other information for the POI. In naming andderiving the type or other information for the POI, heuristics based ontravel time and behavior, for example, can imply a POI type. Once thePOI location has identified, a subset of traces within the selectedgroup are selected which exhibit uncharacteristic delays compared to theoverall control population for the particular maneuver. Theseuncharacteristic delays are then analyzed for time of day, time of week,etc. Any number of heuristic rules based on the culture and customs ofthe area can be applied. For example, certain areas may exhibitdifferent socially accepted times for various meals (e.g. delay at suchcharacteristic times may indicate restaurant), for worship (e.g. delayat such times may indicate a place of worship), for hotel check-in, etc.Other heuristics could indicate a window of time during which a POI isoperational, wherein the delays that deemed to be arrival times may beanalyzed and compared for week days versus weekends, thus indicatingdifferent times of operation, such as M-F 8:00am to 7:00pm, Saturday8:00am to 5:00pm, and closed Sunday, for example. In addition,heuristics can be used to compare similar types of POI, such as hotels,for example, to indicate certain hotels as being preferred over otherhotels based on frequency of occurrences.

Each of the aforementioned pieces of information obtained can beautomatically attributed to the entrance point of the POI, or they canbe slated for manual entering upon further investigation. In the case offrequency of POI visits, the information can be used to prioritize themanual research for verification purposes—that is, new POI locationsreceiving the most delays that are deemed to be arrivals can be givenhighest priority for study. Accordingly, a POI database of mostfrequently visited sites can be corroborated first.

It should be recognized that the airport example discussed above can beapplied to virtually any scenario, particularly those locations having awell defined exit route, wherein vehicles leaving the location can bedifferentiated from a general population.

Obviously, many modifications and variations of the present inventionare possible in light of the above teachings. It is, therefore, to beunderstood that within the scope of the appended claims, the inventionmay be practiced otherwise than as specifically described.

1. A method of analyzing points of interest using traces from probedata, comprising: providing a database of a digital vector mapconfigured to store a plurality of traces representing roads of anavigable street network; collecting probe data from vehicles travelingalong said traces; bundling a group of select traces having routes witha common origin and at least one divergence point downstream from saidorigin; building a database of vehicle maneuvers over said routes;computing at least one of average speeds, delay time, or delay profile,of said group of select traces of vehicles traversing said vehiclemaneuvers; computing at least one of average speeds, delay time, ordelay profile, of all vehicles traversing said vehicle maneuvers;comparing the at least one of average speeds, delay time, or delayprofile from the said group of select traces of vehicles with the atleast one of average speeds, delay time, or delay profile from allvehicles traversing said vehicle maneuvers; and determining, from saidcomparison, information associated with one or more points of interestalong said routes.
 2. The method of claim 1 in which said determininginformation associated with one or more points of interest along saidroutes comprises: determining at least one of the location, type ofestablishment, or hours of operation, of a point of interest along saidroutes.
 3. The method of claim 1 further including calculating the skewof the delay times.
 4. The method of claim 1 further includingcalculating the kurtosis of the delay times.
 5. The method of claim 1further including performing the computing steps during at least one ofa predetermined time of day, week, month and year.
 6. The method ofclaim 1 further including generating a profile of specific locationsalong the routes of the delay times.
 7. A method of analyzing points ofinterest using traces from probe data, comprising: providing a databaseof a digital vector map configured to store a plurality of tracesrepresenting a navigable network; collecting probe data from travelerstraveling along said traces; bundling a group of select traces havingroutes with a common origin and at least one divergence point downstreamfrom said origin; building a database of maneuvers over said routes;computing at least one of average speeds, delay time, or delay profile,of said group of select traces of travelers traversing said maneuvers;computing at least one of average speeds, delay time, or delay profile,of all travelers traversing said maneuvers; comparing the at least oneof average speeds, delay time, or delay profile from the said group ofselect traces of travelers with the at least one of average speeds,delay time, or delay profile from all travelers traversing saidmaneuvers; and determining, from said comparison, at least one of thelocation, type of establishment, or hours of operation, of a point ofinterest along said routes.